Multisource Imprecise Information Calibration for Reliability Assessment of Multistate Systems: A Consensus Reaching Perspective

In system reliability assessment, expert opinions are oftentimes elicited to cope with the issue of poor quantity of failure data. However, information from expert subjective judgments may exhibit imprecision and be elicited from multiple physical levels of a system. Moreover, the elicited information may be conflicting as experts own varying backgrounds of knowledge as well as differentiated cognitive levels, which, thereby, cannot reach a consistent reliability estimate. In this article, we aim at conducting the reliability assessment of multistate systems by fusing conflicting multisource imprecise information (MSII) from the consensus reaching perspective. We utilize an aggregation operator to fuse individual opinions into a collective opinion in the form of mass functions. Evidence distance is, then, adopted to quantify the dissimilarity between individual and collective opinions, and accordingly, a measurement of the average degree of consensus is proposed. In this way, the consensus reaching model is formulated by minimizing the total calibration of MSII with the constraint of a predetermined consensus threshold. The consensus reaching model is resolved by a feasibility-based particle swarm optimization algorithm. A numerical example, along with an application of control rod drive mechanism in nuclear reactors, is used for the demonstration of the effectiveness of the proposed method.